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Angelo Rodio is a Postdoctoral Researcher at Linköping University, Sweden, specializing in decentralized machine learning. He completed his PhD at Inria, France, where he focused on federated learning. His work aims to improve the efficiency and effectiveness of machine learning algorithms operating in decentralized environments. Angelo has authored several publications addressing key challenges in the field, including methods for optimizing communication in federated learning settings and analyzing the convergence of semi-decentralized learning systems. His contributions are recognized in the research community, and he actively engages in developing innovative solutions to enhance collaborative learning among distributed systems.
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